Open AccessTemperature prediction model for solar greenhouse based on improved BP neural network
Author(s) -
Xingjian Li,
Xiangnan Zhang,
Yawei Wang,
KaifengZhang,
Yifei Chen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1639/1/012036
Subject(s) - greenhouse , artificial neural network , environmental science , relative humidity , mean radiant temperature , solar greenhouse , approximation error , mean squared error , mean absolute error , meteorology , humidity , atmospheric sciences , computer science , mathematics , statistics , climate change , geography , artificial intelligence , physics , ecology , horticulture , biology
In the production of solar greenhouse, indoor temperature is a very important index, which is closely related to the growth of crops. In order to solve the problem of hysteretic nature in solar greenhouse temperature regulation, a solar greenhouse temperature prediction model was proposed in this paper. The model took three indoor parameters including temperature, humidity and light intensity of current time in solar greenhouse as inputs and was built on the base of a kind of BP neural network algorithm which was improved by nearest neighbor algorithm, and accurate prediction results were obtained. The mean absolute error (MAE), mean relative error (MRE) and maximal absolute error (MaxE) of the predicted value of the model were 0.89°C, 4.66% and 2.23°C, respectively. All of the three indexes were greatly improved compared with the model without improvement. The model was able to predict the indoor temperature of the greenhouse accurately and as well provide technical support and decision support for the temperature regulation system of the solar greenhouse.